Neural networks have attracted much interest in the last two decades for their potential to realistically describe brain functions, but so far they have failed to provide models that can be simulated in a reasonable time on computers; rather they have been limited to toy models. Quantum computing is a possible candidate for improving the computational efficiency of neural networks. In this framework of quantum computing, the Qubit neuron model, proposed by Matsui and Nishimura, has shown a high efficiency in solving problems such as data compression. Simulations have shown that the Qubit model solves learning problems with significantly improved efficiency as compared to the classical model. In this paper, we confirm our previous results in further detail and investigate what contributes to the efficiency of our model through 4-bit and 6-bit parity check problems, which are known as basic benchmark tests. Our simulations suggest that the improved performance is due to the use of superposition of neural states and the use of probability interpretation in the observation of the output states of the model.
The Qubit neuron model is a new non-standard computing scheme that has been found by simulations to have efficient processing abilities. In this paper we investigate the usefulness of the model for a non linear kinetic control application of an inverted pendulum on a cart. Simulations show that a neural network based on Qubit neurons would swing up and stabilize the pendulum, yet it also requires a shorter range over which the cart moves as compared to a conventional neural network model.
Key words. inverted pendulum, quantum neural network, qubit, swing up controllAbbreviations. CNN -conventional neural network; QBP -quantum back propagation; QNN -qubit neural network
SUMMARYWith the development of our highly information-oriented society, there is an increasing demand for large-scale and high-level information processing. Toward this goal, studies have sought to create a new computation principle having an information processing ability exceeding the existing Neumann-type computer, such as the creation of a new computation theory or the integration of the frameworks of existing computation theories. As one such approach, quantum neural computing is considered to be interesting, which integrates neural computing and quantum computation. This paper constructs the feed-forward neural network, which is widely used in practice, based on the qubit neuron model. The 4-bit parity-check problem and the general function identification problem are considered. The performance is compared to the feed-forward network based on the conventional neuron model, and it is shown that the proposed model has a higher performance than the conventional model, using the learning diagram composed of convergence rate and the number of learning iterations. The reason for the better performance is also discussed.
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